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Once we have decided on the specifics of our model, we need to do two processes: Compile the model and fit the data to the model.
We can compile the model like so:
`model.compile(optimizer='sgd', l…
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With @ericmjl, @rlouf.
## Current state
MCX manages its own graph by reading source code, translates it into a networkx
graph. The graph is compiled into samplers and a logpdf. It can be inspec…
rlouf updated
4 years ago
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Once we have decided on the specifics of our model, we need to do two processes: Compile the model and fit the data to the model.
We can compile the model like so:
`model.compile(optimizer='sgd', l…
-
Once we have decided on the specifics of our model, we need to do two processes: Compile the model and fit the data to the model.
We can compile the model like so:
`model.compile(optimizer='sgd', l…
-
Once we have decided on the specifics of our model, we need to do two processes: Compile the model and fit the data to the model.
We can compile the model like so:
`model.compile(optimizer='sgd', l…
-
While the [QuBase.jl](https://github.com/JuliaQuantum/QuBase.jl) project is defining a basic typing system for high-level and specific projects, quantum dynamics calculation functions are encouraged t…
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When using 1 or 2 GPUs there's no problem. When using 3 or more GPUs the issue manifests itself resulting in corrupted computations. This was tested on 4 V100 GPUs. There's a workaround (see commented…
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Hi @williamFalcon,
I have been trying to overfit SimCLR on a single batch containing 2 images. I added breakpoints and noticed that each time the same batch is loaded on subsequent epochs, the tran…
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Once we have decided on the specifics of our model, we need to do two processes: Compile the model and fit the data to the model.
We can compile the model like so:
`model.compile(optimizer='sgd', l…
-
## Background
**What is your motivation?**
Simulate stochastic processes, starting for finite space and discrete-time Markov Chains, going to countable space and discrete-time, then countable spac…